Assessing Drivers’ Trust in Automated Driving Systems: An Integrated Study
DOI: 10.54941/ahfe1002465
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study addresses the critical role of driver trust in the adoption and safe operation of automated driving systems (AVs). As AV technology evolves from Level 2 assistance to Level 5 full automation, trust becomes a primary determinant of human-machine interaction, influencing whether drivers misuse, disuse, or appropriately rely on the system. The research aims to build a comprehensive framework for assessing both initial and dynamic trust by integrating demographic surveys with experimental simulator data. This approach combines subjective self-reports with behavioral and physiological measures to provide a more holistic understanding of trust formation and fluctuation than previous studies relying solely on questionnaires or isolated experiments. The methodology employed two mixed-method studies. First, an online questionnaire was administered to 1,131 drivers in China to assess initial trust, collecting demographic data and responses on dispositional and learned trust factors using a 7-point Likert scale. Second, a simulator experiment involved 26 participants who completed six driving sessions of varying road complexity in an L3 automated driving environment. This phase utilized a VR helmet for gaze tracking, a steering wheel for acceleration and braking data, and iMotions software to record electrocardiogram (ECG) and galvanic skin response (GSR) data. Participants rated their trust after each session, and auditory reminders were introduced in specific scenarios to test their impact on situational awareness. The results indicate that initial trust is significantly influenced by individual differences, including age, gender, driving experience, and technological acceptance. Specifically, younger male drivers with more driving years, higher self-perceived driving capacity, and greater understanding of AV technology reported higher initial trust. Conversely, higher concerns regarding privacy and safety risks correlated with lower trust. In the dynamic trust assessment, trust levels generally increased after simulator exposure, particularly among participants with no prior AV experience. Physiological and behavioral data revealed that higher road complexity correlated with increased heart rate and skin conductance peaks, indicating higher stress and lower trust. Crucially, the study found that audible reminders significantly enhanced situational awareness and trust in high-risk scenarios involving pedestrians and obstacles, outperforming visual-only cues in perception and understanding metrics. The significance of this research lies in its integrated framework for trust assessment, demonstrating that trust is not static but dynamically calibrated through interaction and environmental context. The findings highlight that individual dispositions set the baseline for trust, which is then modulated by system performance and situational factors. The effectiveness of auditory reminders suggests that multi-modal human-machine interfaces can mitigate trust deficits in complex scenarios. These insights provide a robust basis for designing AV systems that foster appropriate trust, thereby improving safety and user acceptance as automated vehicles become commercially viable.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- trust calibration
- trust in automation foundations
- acceptance adoption
- automation
- automation surprise
- situational awareness
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: self report data
- Methodological Resource: validation psychometrics
- Theoretical Contribution: computational model